Code to reproduce the analyses of the AWST paper: Analysis of the Synthetic data
Figure 2 and 3,
Supplementary Figures 1, 2, 3, and 4.,
Supplementary table 2,
Updated after review.
S.M.Pagnotta - Dept. of Science and Technology, Universita’ degli Studi del Sannio (Benevento, Italy) D.Risso - Dept. of Statistical Sciences, Universita’ degli Studi di Padova (Padova, Italy)
22 ottobre, 2020
Here we show the code to reproduce the analyses of: Risso and Pagnotta (2020). Per-sample standardization and asymmetric winsorization lead to accurate classification of RNA-seq expression profiles. In preparation.
This file belongs to the repository: https://github.com/drisso/awst_analysis.
The code is released with license GPL v3.0.
Install and load awst
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("drisso/awst")The synthetic dataset
… We then created \(k=5\) groups of samples, each made of \(M=30\) replicate samples, randomly selected (with replacement) from the original set of 80. For each group, we randomly selected (without replacement) \(J=500\) genes, whose expression was altered according to the following multiplicative model. \[ \tilde{y}_j = y_j \cdot (0.001 + r_j), \quad j=1,\ldots,J, \] where \(y_j\) denotes the observed expression of gene \(j\), \(\tilde{y}\) denotes the perturbed expression, and \(r_j\) is the realization of a Gamma random variable with shape parameter \(a=0.5\) and scale parameter \(s=1\).
#BiocManager::install("seqc") # uncomment if necessary
rm(list = ls())
set.seed(20200413)
library(seqc)
ddata <- get("ILM_aceview_gene_MAY")
ddata <- ddata[!is.na(ddata$EntrezID),]
ddata <- ddata[!ddata$IsERCC,]
feature_annotation <- data.frame(ddata[, 1:3], row.names = ddata$EntrezID)
dim(ddata <- as.matrix(ddata[, -c(1:4)])) #[1] 19701 384
sum(duplicated(feature_annotation$Symbol)) #[1] 0
sum(duplicated(feature_annotation$EntrezID)) #[1] 0
row.names(ddata) <- feature_annotation$EntrezID
dim(sample_annotation.df <- data.frame(row.names = colnames(ddata), sample = colnames(ddata))) #[1] 384 1
sample_annotation.df$lane <- substr(sample_annotation.df$sample, 6, 7)
sample_annotation.df$flow_cell <- substr(sample_annotation.df$sample, 17, 17)
sample_annotation.df$sample <- substr(sample_annotation.df$sample, 1, 3)
sample_annotation.df <- sample_annotation.df[grep("A", sample_annotation.df$sample),]
dim(ddata <- ddata[, rownames(sample_annotation.df)]) #[1] 19701 80
M <- 30
nnumbers <- c("01", "02", "03", "04", "05", "06", "07", "08", "09", paste(10:M))
tmp <- c(paste0("A", nnumbers), paste0("B", nnumbers), paste0("C", nnumbers),
paste0("D", nnumbers), paste0("E", nnumbers))
design.df <- data.frame(row.names = tmp, sample = tmp, original.sample = NA)
design.df$original.sample[1:M] <- sample(rownames(sample_annotation.df), M, replace = TRUE)
design.df$original.sample[(M+1):(2*M)] <- sample(rownames(sample_annotation.df), M, replace = TRUE)
design.df$original.sample[(2*M+1):(3*M)] <- sample(rownames(sample_annotation.df), M, replace = TRUE)
design.df$original.sample[(3*M+1):(4*M)] <- sample(rownames(sample_annotation.df), M, replace = TRUE)
design.df$original.sample[(4*M+1):(5*M)] <- sample(rownames(sample_annotation.df), M, replace = TRUE)
# table(design.df$original.sample)
k <- "A"
wwhich <- grep(k, design.df$sample)
synthetic_data <- ddata[, design.df$original.sample[wwhich]]
colnames(synthetic_data) <- design.df$sample[wwhich]
the_genes <- rownames(synthetic_data)
no_of_altered_genes <- 500
genes_to_alterate <- sample(the_genes, no_of_altered_genes, replace = FALSE)
the_genes <- the_genes[-which(the_genes %in% genes_to_alterate)]
for(jj in 1:M) {
tmp <- 0.001 + rgamma(length(genes_to_alterate), shape = 0.5, scale = 1)
synthetic_data[genes_to_alterate, jj] <- synthetic_data[genes_to_alterate, jj] * tmp
}
# k <- "B"
for(k in c("B", "C", "D", "E")) {
wwhich <- grep(k, design.df$sample)
tmp_data <- ddata[, design.df$original.sample[wwhich]]
colnames(tmp_data) <- design.df$sample[wwhich]
genes_to_alterate <- sample(the_genes, no_of_altered_genes, replace = FALSE)
the_genes <- the_genes[-which(the_genes %in% genes_to_alterate)]
for(jj in 1:M) {
tmp <- 0.001 + rgamma(length(genes_to_alterate), shape = 0.5, scale = 1)
tmp_data[genes_to_alterate, jj] <- tmp_data[genes_to_alterate, jj] * tmp
}
synthetic_data <- cbind(synthetic_data, tmp_data)
}
dim(ddata <- floor(synthetic_data)) #[1] 19701 150
annotation.df <- data.frame(samples = colnames(ddata), row.names = colnames(ddata))
annotation.df$sample <- substr(annotation.df$samples, 1, 1)
annotation.df$sample.col <- factor(annotation.df$sample)
levels(annotation.df$sample.col) <- clust.col <- c("gold", "red", "green2", "blue", "cyan")
names(clust.col) <- unique(annotation.df$sample)
save(ddata, annotation.df, feature_annotation, clust.col, file = "synthetic20200413.RData")
#tmp <- cbind(annotation.df, t(ddata))
#write.table(tmp, file = "synthetic20200413.tsv", sep = "\t", quote = FALSE, row.names = FALSE)
#write.table(feature_annotation, file = "synthetic20200413_features_annotation.tsv", sep = "\t", quote = FALSE, row.names = FALSE)Figure 2
Performance of different data pre-processing paired with the hierarchical clustering (Euclidean distance, Ward’s linkage). a) AWST data pre-processing; b) Hart’s data pre-processing; c) Radovich’s data pre-processing; d) TCGA data pre-processing. The “sample” bar indicates the true partition, and the “clust” bar indicates the inferred partition obtained by cutting the tree to get 5 clusters. The Calinski-Harabasz curve is superimposed in each panel.
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Supplementary Figure 1
(Extended Figure 2) Performance of different data pre-processing paired with the hierarchical clustering (Euclidean distance, Ward’s linkage). a) AWST data pre-processing; b) Hart’s data pre-processing; c) Radovich’s data pre-processing; d) TCGA data pre-processing; e) FPKM pre-processing with top 2,500 features according to standard-deviation; f) FPKM pre-processing with top 5,000 features according to standard-deviation; g) rlog pre-processing; h) VST pre-processing; i) Townes’ transformation (null residuals with deviance); l) Townes’ transformation (null residuals with pearson). The “sample” bar indicates the true partition, and the “clust” bar indicates the inferred partition obtained by cutting the tree to get 5 clusters. The Calinski-Harabasz curve is superimposed in each panel.|
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Supplementary Figure 2
Study for the synthetic data of the effects of data pre-processing on the compactness of groups respect to the theoretical partition and Euclidean distance. a) AWST; b) Hart; c) Radovich; d) TCGA; e) FPKM pre-processing with top 2500 features according to standard-deviation; f) FPKM pre-processing with top 5000 features according to standard-deviation; g) rlog pre-processing; h) VST pre-processing; i) Townes’ transformation (null residuals with deviance); l) Townes’ transformation (null residuals with pearson).
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Supplementary Figure 3
Performance of different data pre-processing paired with ConsensusClusterPlus (inner and outer average linkage with Pearson’s correlation as distance matrix). a) AWST data pre-processing; b) Hart’s data pre-processing; c) Radovich’s protocol; d) TCGA data protocol; e) FPKM pre-processing with top 2,500 features according to standard-deviation; f) FPKM pre-processing with top 5,000 features according to standard-deviation; g) rlog pre-processing; h) VST pre-processing; i) Townes’ transformation (null residuals with deviance); l) Townes’ transformation (null residuals with pearson). The “sample’’ bar indicates the true partition, and the”consensus’’ bar indicates the inferred partition obtained by requiring 5 clusters.
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Supplementary Figure 4
Performance of different data pre-processing paired with ConsensusClusterPlus (Euclidean distance and PAM method). a) AWST data pre-processing; b) Hart’s data pre-processing; c) Radovich’s pre-processing; d) TCGA data pre-processing; e) FPKM pre-processing with top 2,500 features according to standard-deviation; f) FPKM pre-processing with top 5,000 features according to standard-deviation; g) rlog pre-processing; h) VST pre-processing; i) Townes’ transformation (null residuals with deviance); l) Townes’ transformation (null residuals with pearson). The “sample’’ bar indicates the true partition, and the”consensus’’ bar indicates the inferred partition obtained by requiring 5 clusters.|
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AWST procedure
hclust (Euclidean/Ward)
## user system elapsed
## 0.919 0.007 0.928
## null device
## 1## null device
## 1## cluster accuracy (eca): 0.9917## cluster purity (ecp): 0.9914## adjusted Rand's index (ari): 0.9832## G index (geometric average of eca, ecp, and ari): 0.9887## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
Result of ConsensuClusterPlus with clusterAlg = “pam” and distance = “euclidean” (other parameters left to default)
## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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Hart 2013
Finding the active genes in deep RNA-seq gene expression studies
hclust (Euclidean/Ward)
## user system elapsed
## 0.609 0.000 0.608
## null device
## 1## null device
## 1## cluster accuracy (eca): 0.9122## cluster purity (ecp): 0.8829## adjusted Rand's index (ari): 0.6779## G index (geometric average of eca, ecp, and ari): 0.8173## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## null device
## 1## cluster accuracy (eca): 0.7377## cluster purity (ecp): 0.9315## adjusted Rand's index (ari): 0.6924## G index (geometric average of eca, ecp, and ari): 0.7806## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
Result of ConsensuClusterPlus with clusterAlg = “pam” and distance = “euclidean” (other parameters left to default) 
## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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Radovich (2018) procedure
The Integrated Genomic Landscape of Thymic Epithelial Tumors
hclust (Euclidean/Ward)
## user system elapsed
## 1.227 0.000 1.227
## null device
## 1## cluster accuracy (eca): 0.6775## cluster purity (ecp): 0.6563## adjusted Rand's index (ari): 0.0848## G index (geometric average of eca, ecp, and ari): 0.3354## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
Result of ConsensuClusterPlus with clusterAlg = “pam” and distance = “euclidean” (other parameters left to default) 
## cluster accuracy (eca): 0.6201## cluster purity (ecp): 0.6072## adjusted Rand's index (ari): 0.0347## G index (geometric average of eca, ecp, and ari): 0.2355## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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TCGA (2015) procedure
Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas - Supplementary Appendix (see pages 23-24)
hclust (Euclidean/Ward)
## user system elapsed
## 3.701 0.044 3.750
## cluster accuracy (eca): 0.3872## cluster purity (ecp): 0.8364## adjusted Rand's index (ari): 0.0013## G index (geometric average of eca, ecp, and ari): 0.0742## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## null device
## 1## cluster accuracy (eca): 0.5595## cluster purity (ecp): 0.6445## adjusted Rand's index (ari): 0.1102## G index (geometric average of eca, ecp, and ari): 0.3412## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
Result of ConsensuClusterPlus with clusterAlg = “pam” and distance = “euclidean” (other parameters left to default) 
## null device
## 1## cluster accuracy (eca): 0.3872## cluster purity (ecp): 0.8364## adjusted Rand's index (ari): 0.0013## G index (geometric average of eca, ecp, and ari): 0.0742## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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FPKM: Top 2500 features
## user system elapsed
## 0.136 0.008 0.145
## null device
## 1## cluster accuracy (eca): 0.881## cluster purity (ecp): 0.8299## adjusted Rand's index (ari): 0.4637## G index (geometric average of eca, ecp, and ari): 0.6973## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## null device
## 1## cluster accuracy (eca): 0.7717## cluster purity (ecp): 0.7711## adjusted Rand's index (ari): 0.5204## G index (geometric average of eca, ecp, and ari): 0.6765## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
Result of ConsensuClusterPlus with clusterAlg = “pam” and distance = “euclidean” (other parameters left to default) 
## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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FPKM: Top 5000 features
## null device
## 1## cluster accuracy (eca): 0.3872## cluster purity (ecp): 0.8364## adjusted Rand's index (ari): 0.0013## G index (geometric average of eca, ecp, and ari): 0.0742## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## null device
## 1## cluster accuracy (eca): 0.6104## cluster purity (ecp): 0.6294## adjusted Rand's index (ari): 0.1812## G index (geometric average of eca, ecp, and ari): 0.4114## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
Result of ConsensuClusterPlus with clusterAlg = “pam” and distance = “euclidean” (other parameters left to default) 
## null device
## 1## cluster accuracy (eca): 0.7841## cluster purity (ecp): 0.8917## adjusted Rand's index (ari): 0.6188## G index (geometric average of eca, ecp, and ari): 0.7564## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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VST (DESeq2): all features
## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”)
## Loading required package: ConsensusClusterPlus## end fraction## clustered
## clustered
## clustered
## clustered
## clustered
## clustered
## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
Result of ConsensuClusterPlus with clusterAlg = “pam” and distance = “euclidean” (other parameters left to default)
## end fraction## clustered
## clustered
## clustered
## clustered
## clustered
## clustered
## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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rLog (DESeq2): all features
## null device
## 1## cluster accuracy (eca): 0.9309## cluster purity (ecp): 0.8527## adjusted Rand's index (ari): 0.3833## G index (geometric average of eca, ecp, and ari): 0.6726## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## null device
## 1## cluster accuracy (eca): 0.8166## cluster purity (ecp): 0.7942## adjusted Rand's index (ari): 0.509## G index (geometric average of eca, ecp, and ari): 0.6911## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
Result of ConsensuClusterPlus with clusterAlg = “pam” and distance = “euclidean” (other parameters left to default) 
## null device
## 1## cluster accuracy (eca): 0.9625## cluster purity (ecp): 0.954## adjusted Rand's index (ari): 0.8968## G index (geometric average of eca, ecp, and ari): 0.9373## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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rLog (DESeq2) 2,500 HVG
## null device
## 1## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”)
## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
Result of ConsensuClusterPlus with clusterAlg = “pam” and distance = “euclidean” (other parameters left to default)
## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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Townes/deviance (scry)
## null device
## 1## null device
## 1## cluster accuracy (eca): 0.9865## cluster purity (ecp): 0.9856## adjusted Rand's index (ari): 0.9669## G index (geometric average of eca, ecp, and ari): 0.9796## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## null device
## 1## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
## null device
## 1## cluster accuracy (eca): 0.9665## cluster purity (ecp): 0.9627## adjusted Rand's index (ari): 0.9166## G index (geometric average of eca, ecp, and ari): 0.9483## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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Townes/deviance (scry) 2,500HVG
## cluster accuracy (eca): 0.9865## cluster purity (ecp): 0.9856## adjusted Rand's index (ari): 0.9669## G index (geometric average of eca, ecp, and ari): 0.9796## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## cluster accuracy (eca): 1## cluster purity (ecp): 1## adjusted Rand's index (ari): 1## G index (geometric average of eca, ecp, and ari): 1## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
## cluster accuracy (eca): 0.971## cluster purity (ecp): 0.9685## adjusted Rand's index (ari): 0.933## G index (geometric average of eca, ecp, and ari): 0.9573## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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Townes/pearson (scry)
## null device
## 1## cluster accuracy (eca): 0.928## cluster purity (ecp): 0.9102## adjusted Rand's index (ari): 0.7555## G index (geometric average of eca, ecp, and ari): 0.861## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## null device
## 1## cluster accuracy (eca): 0.9264## cluster purity (ecp): 0.9208## adjusted Rand's index (ari): 0.8227## G index (geometric average of eca, ecp, and ari): 0.8886## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
## null device
## 1## cluster accuracy (eca): 0.9297## cluster purity (ecp): 0.9172## adjusted Rand's index (ari): 0.805## G index (geometric average of eca, ecp, and ari): 0.8821## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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Townes/pearson (scry) 2,500HVG
## cluster accuracy (eca): 0.9689## cluster purity (ecp): 0.9625## adjusted Rand's index (ari): 0.9011## G index (geometric average of eca, ecp, and ari): 0.9437## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5ConsensusClusterPlus (default parameters)
Result of ConsensuClusterPlus with default setup
(innerLinkage=“average”, finalLinkage=“average”, distance=“pearson”) 
## null device
## 1## cluster accuracy (eca): 0.9826## cluster purity (ecp): 0.9809## adjusted Rand's index (ari): 0.9513## G index (geometric average of eca, ecp, and ari): 0.9715## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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ConsensusClusterPlus (PAM)
## null device
## 1## cluster accuracy (eca): 0.9601## cluster purity (ecp): 0.9534## adjusted Rand's index (ari): 0.8845## G index (geometric average of eca, ecp, and ari): 0.932## no. of clusters in theoretical partition: 5## no. of clusters in estimated partition: 5
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Table of the experiments
| what | with | ECA | ECP | ARI | G | ASW | timing |
|---|---|---|---|---|---|---|---|
| AWST | HC | 0.9917 | 0.9914 | 0.9832 | 0.9887 | 0.2465 | 0.848 |
| AWST | CCP1 | 1 | 1 | 1 | 1 | NA | NA |
| AWST | CCP2 | 1 | 1 | 1 | 1 | NA | NA |
| Hart | HC | 0.9122 | 0.8829 | 0.6779 | 0.8173 | 0.1832 | 0.58 |
| Hart | CCP1 | 0.7377 | 0.9315 | 0.6924 | 0.7806 | NA | NA |
| Hart | CCP2 | 1 | 1 | 1 | 1 | NA | NA |
| Radovich | HC | 0.6775 | 0.6563 | 0.0848 | 0.3354 | 0.0449 | 1.135 |
| Radovich | CCP1 | 1 | 1 | 1 | 1 | NA | NA |
| Radovich | CCP2 | 0.6201 | 0.6072 | 0.0347 | 0.2355 | NA | NA |
| TCGA | HC | 0.3872 | 0.8364 | 0.0013 | 0.0742 | 0.0345 | 3.335 |
| TCGA | CCP1 | 0.5595 | 0.6445 | 0.1102 | 0.3412 | NA | NA |
| TCGA | CCP2 | 0.3872 | 0.8364 | 0.0013 | 0.0742 | NA | NA |
| FPKM (2,500HVG) | HC | 0.881 | 0.8299 | 0.4637 | 0.6973 | 0.178 | 0.134 |
| FPKM (2,500HVG) | CCP1 | 0.7717 | 0.7711 | 0.5204 | 0.6765 | NA | NA |
| FPKM (2,500HVG) | CCP2 | 1 | 1 | 1 | 1 | NA | NA |
| FPKM (5,000HVG) | HC | 0.3872 | 0.8364 | 0.0013 | 0.0742 | 0.0864 | NA |
| FPKM (5,000HVG) | CCP1 | 0.6104 | 0.6294 | 0.1812 | 0.4114 | NA | NA |
| FPKM (5,000HVG) | CCP2 | 0.7841 | 0.8917 | 0.6188 | 0.7564 | NA | NA |
| VST | HC | 1 | 1 | 1 | 1 | 0.2504 | 3.068 |
| VST | CCP1 | 1 | 1 | 1 | 1 | NA | NA |
| VST | CCP2 | 1 | 1 | 1 | 1 | NA | NA |
| rLog | HC | 0.9309 | 0.8527 | 0.3833 | 0.6726 | 0.1728 | 927.896 |
| rLog | CCP1 | 0.8166 | 0.7942 | 0.509 | 0.6911 | NA | NA |
| rLog | CCP2 | 0.9625 | 0.954 | 0.8968 | 0.9373 | NA | NA |
| rLog (2,500HVG) | HC | 1 | 1 | 1 | 1 | 0.2533 | NA |
| rLog (2,500HVG) | CCP1 | 1 | 1 | 1 | 1 | NA | NA |
| rLog (2,500HVG) | CCP2 | 1 | 1 | 1 | 1 | NA | NA |
| Townes/deviance | HC | 0.9865 | 0.9856 | 0.9669 | 0.9796 | 0.158 | 0.216 |
| Townes/deviance | CCP1 | 1 | 1 | 1 | 1 | NA | NA |
| Townes/deviance | CCP2 | 0.9665 | 0.9627 | 0.9166 | 0.9483 | NA | NA |
| Townes/deviance (2,500HVG) | HC | 0.9865 | 0.9856 | 0.9669 | 0.9796 | 0.191 | NA |
| Townes/deviance (2,500HVG) | CCP1 | 1 | 1 | 1 | 1 | NA | NA |
| Townes/deviance (2,500HVG) | CCP2 | 0.971 | 0.9685 | 0.933 | 0.9573 | NA | NA |
| Townes/pearson | HC | 0.928 | 0.9102 | 0.7555 | 0.861 | 0.085 | 0.048 |
| Townes/pearson | CCP1 | 0.9264 | 0.9208 | 0.8227 | 0.8886 | NA | NA |
| Townes/perason | CCP2 | 0.9297 | 0.9172 | 0.805 | 0.8821 | NA | NA |
| Townes/pearson (2,500HVG) | HC | 0.9689 | 0.9625 | 0.9011 | 0.9437 | 0.1099 | NA |
| Townes/pearson (2,500HVG) | CCP1 | 0.9826 | 0.9809 | 0.9513 | 0.9715 | NA | NA |
| Townes/pearson (2,500HVG) | CCP2 | 0.9601 | 0.9534 | 0.8845 | 0.932 | NA | NA |
HC) hirerachical clustering with Ward’s likage and Euclidean distance; CPP1) ConsensusClusterPlus with average innner and outer linkage, and Pearson’s correlation as distance; CCP2) ConsensusClusterPlus with PAM and Euclidean distance;
Average Silhouettes Width
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